{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pymc4 as pm\n",
    "import arviz as az"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "az.style.use('arviz-darkgrid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy.random as npr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PyMC4 Made Simple for PyMC3 Users\n",
    "\n",
    "In this notebook, we will use a simple Bayesian estimation example to learn how to use the PyMC4 syntax."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simulated Data\n",
    "\n",
    "To keep things simple, we will use a simple example in which we generate 1,000 data points from a normal distribution with a pre-specified $\\mu$ and $\\sigma$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "MU = 8\n",
    "SIG = 2.2\n",
    "\n",
    "data = np.random.normal(loc=MU, scale=SIG, size=200)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The goal of our estimation task is to estimate the true value of $\\mu$ and $\\sigma$ from the observed data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Definition\n",
    "\n",
    "PyMC models are defined as functions with a `@pm.model` decorator on top.\n",
    "\n",
    "To specify random variables as Python objects, we use the `yield` statement. This stems from PyMC4's API design, which uses coroutines underneath the hood. The technical reason is documented in the PyMC4 design guide; what's cool here is that it also serves as a \"visual hack\" that lets you very quickly identify all of your random variables in a model. \n",
    "\n",
    "We're going to define our model in the code cell below.\n",
    "\n",
    "As a prior for the $\\mu$, we will use a relatively flat Normal distribution, and for the $\\sigma$ prior, a relatively flat Exponential distribution.\n",
    "\n",
    "Unlike PyMC3's distributions, which used spelled Greek letters as arguments, in PyMC4, we use the standardized \"location\", \"scale\", \"rate\" and \"concentration\" paradigm used by TensorFlow Probability's distributions, as well as NumPy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "@pm.model\n",
    "def model(data):\n",
    "    mu = yield pm.Normal(loc=0, scale=10, name=\"mu\")\n",
    "    sig = yield pm.Exponential(rate=0.1, name=\"sig\")\n",
    "    \n",
    "    like = yield pm.Normal(loc=mu, scale=sig, observed=data, name=\"like\")\n",
    "    \n",
    "    return like"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sampling from Posterior\n",
    "\n",
    "Sampling from posterior distributions is more or less the same as in PyMC3.\n",
    "\n",
    "We call on the model function, and then pass the result to `pm.sample`.\n",
    "\n",
    "Unlike PyMC3, `pm.sample(model)` now returns both the trace _and_ the computed sampling stats.\n",
    "\n",
    "Give it a moment to sample; as of the alpha version of PyMC4, the progress bar is unavailable because sampling is also delegated to TensorFlow probability."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "estimation_model = model(data)\n",
    "\n",
    "trace = pm.sample(model(data), num_samples=800)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The trace returned is an ArviZ's InferenceData object, which is the central data format for ArviZ. InferenceData itself is just a container that maintains references to one or more `xarray.Dataset`. You can check the InferenceData structure specification [here](https://arviz-devs.github.io/arviz/schema/schema.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing Posterior Distributions\n",
    "\n",
    "Visualizations were completely delegated to `arviz` in PyMC3, and that is the same in PyMC4."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "az.plot_posterior(trace, var_names=\"model/mu\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We recovered the true $\\mu$!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "az.plot_posterior(trace, var_names=\"model/__log_sig\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we take the exponent of the posterior distribution trace values, we will recover back approximately 2.2 for the $\\sigma$ as well."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prior/Posterior Predictive Samples\n",
    "\n",
    "Doing prior and posterior predictive sampling is an important part of the modelling workflow. Just like in PyMC3, this functionality has been added to PyMC4. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Note that RandomStandardNormal inside pfor op may not give same output as inside a sequential loop.\n",
      "WARNING:tensorflow:Note that RandomUniform inside pfor op may not give same output as inside a sequential loop.\n",
      "WARNING:tensorflow:Note that RandomStandardNormal inside pfor op may not give same output as inside a sequential loop.\n"
     ]
    }
   ],
   "source": [
    "draws_prior = pm.sample_prior_predictive(estimation_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = az.plot_kde(draws_prior.prior_predictive['model/like'], label='prior_predictive');\n",
    "ax = az.plot_kde(data, label='data', plot_kwargs={'color':'C1'});"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`draws_prior` Is also an InferenceData object as such is ordered into groups, in this case we have a single group `prior_predictive`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Inference data with groups:\n",
       "\t> prior_predictive"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "draws_prior"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We drew 1,000 i.i.d. samples from the prior distributions for each of the random variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:     (chain: 1, draw: 1000)\n",
       "Coordinates:\n",
       "  * chain       (chain) int64 0\n",
       "  * draw        (draw) int64 0 1 2 3 4 5 6 7 ... 992 993 994 995 996 997 998 999\n",
       "Data variables:\n",
       "    model/mu    (chain, draw) float32 29.862984 -5.0432544 ... 11.291338\n",
       "    model/sig   (chain, draw) float32 13.909912 3.2487392 ... 10.877745\n",
       "    model/like  (chain, draw) float32 25.10049 -11.890129 ... 18.944897\n",
       "Attributes:\n",
       "    created_at:  2020-01-08T14:35:31.249828"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "draws_prior.prior_predictive"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let make predictions from the fitted model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Note that RandomStandardNormal inside pfor op may not give same output as inside a sequential loop.\n"
     ]
    }
   ],
   "source": [
    "draws_posterior = pm.sample_posterior_predictive(estimation_model, trace, \n",
    "                                                 inplace=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`draws_posterior` Is also an InferenceData object, with the group `posterior_predictive`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Inference data with groups:\n",
       "\t> posterior_predictive"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "draws_posterior"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alternatively, we could have called `pm.sample_posterior_predictive` with the argument `inplace=True` to add the `posterior_predictive` group to `trace` instead of generating a new object.\n",
    "\n",
    "Let's see what we got from sampling from the posterior."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:           (chain: 10, draw: 800, model/like_dim_0: 200)\n",
       "Coordinates:\n",
       "  * chain             (chain) int64 0 1 2 3 4 5 6 7 8 9\n",
       "  * draw              (draw) int64 0 1 2 3 4 5 6 ... 793 794 795 796 797 798 799\n",
       "  * model/like_dim_0  (model/like_dim_0) int64 0 1 2 3 4 ... 195 196 197 198 199\n",
       "Data variables:\n",
       "    model/like        (chain, draw, model/like_dim_0) float32 10.516926 ... 9.981089\n",
       "Attributes:\n",
       "    created_at:  2020-01-08T14:35:31.748422"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "draws_posterior.posterior_predictive"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We had 10 chains, 800 samples for each observation (1st and 2nd dim) and 1000 observations.\n",
    "\n",
    "To make this clearer, Luciano Paz (one of the PyMC developers) provided the following explanation:\n",
    "\n",
    "> The shape of the output for a posterior predictive samples is (n_chains, samples_per_chain) + shape_of_rv output\n",
    ">\n",
    "> The shape of the RV in posterior predictive sampling takes the shape of the supplied observed values into account. This is not like what prior predictive sampling does, where it only cares about the shape of the distribution."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "InferenceData objects can be easily concatenated, so you can have all the relevant data in the same place"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined_trace = trace + draws_posterior + draws_prior"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Inference data with groups:\n",
       "\t> posterior\n",
       "\t> posterior_predictive\n",
       "\t> sample_stats\n",
       "\t> prior_predictive\n",
       "\t> observed_data"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_trace"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is handy for example when performing posterior predictive checks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/osvaldo/anaconda3/lib/python3.7/site-packages/IPython/core/events.py:88: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
      "  func(*args, **kwargs)\n",
      "/home/osvaldo/anaconda3/lib/python3.7/site-packages/IPython/core/pylabtools.py:128: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "az.plot_ppc(combined_trace);"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
